Data-Driven Prediction of Global Automotive Trends: Forecasting Fuel Economy and CO₂ Emissions Using Machine Learning

In the pursuit of cleaner transportation and environmental sustainability, the global automotive industry is undergoing rapid transformation. This study leverages historical multi-decade vehicle data to develop predictive models for fuel economy and CO₂ emissions across a wide range of vehicle technologies. By utilizing advanced machine learning algorithms in Python and integrating insights through Power BI visualizations, the project identifies key correlations between vehicle attributes—such as weight, powertrain, and footprint—and their environmental performance. Results highlight the increasing impact of electric vehicle adoption, hybridization, and light weighting on overall emissions reduction. These insights help forecast the direction of fuel economy standards, emission patterns, and technology shifts across manufacturers and vehicle types. Beyond technical predictions, the study offers a decision-support framework for global policymakers, automotive designers, and sustainability advocates. The outcomes underscore the importance of data-driven approaches to enhance regulatory compliance, shape product innovation, and drive sustainable mobility solutions at a global level. Keywords: Machine Learning, Automotive Trends, Predictive Analytics, Data Visualization